Data hotness analysis method and device for power grid service resource data, computer device and readable storage medium

Abstract

The application relates to a power grid service resource data data heat analysis method and device, computer equipment and a readable storage medium. The method comprises the following steps: determining the use heat quantification value of at least two service data tables associated with power grid service resource data according to the data call record of the power grid service front-end application to the power grid service resource data; sorting the service data tables according to the use heat quantification value of each service data table to obtain a service data table sorting result; determining target heat service data according to the service data table sorting result; the use heat of the target heat service data meets a preset condition; processing and handling the target heat service data according to the distribution of the target heat service data in a target service data table to which the target heat service data belongs, and displaying a service data table data heat diagram. The method can improve the analysis efficiency of power grid service resource data.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a data heat analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power grid business resource data. Background Technology

[0002] Currently, in the process of processing power grid business resource data, it is very difficult to accurately identify high-value data from massive and complex data. This makes it difficult for power grid business resource data to fully play its supporting role in data management, resource allocation and business optimization, resulting in a large amount of valuable information being submerged in the data deluge.

[0003] Traditional technologies often use a single indicator to simply measure data usage, ignoring the actual distribution characteristics of high-frequency business data in the relevant data tables. This makes it difficult to accurately quantify the actual usage status of power grid business resource data and adapt to the actual needs of power grid digitalization business for efficient utilization of resource data.

[0004] Therefore, traditional technologies suffer from low accuracy in describing the actual usage status of power grid business resource data. Summary of the Invention

[0005] Therefore, it is necessary to provide a data heat analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power grid business resource data that can improve the accuracy of describing the actual data usage status of power grid business resource data, thereby addressing the aforementioned technical problems.

[0006] Firstly, this application provides a method for analyzing the heat of power grid business resource data, including:

[0007] Based on the data call records of the power grid business front-end application to the power grid business resource data, determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data;

[0008] Based on the usage heat quantification value of each business data table, sort each business data table to obtain the business data table sorting result;

[0009] Based on the sorting results of the business data table, target popularity business data is determined; the usage popularity of the target popularity business data meets preset conditions.

[0010] Based on the distribution of the target popularity business data in the corresponding target business data table, the target popularity business data is processed to display a data heat map of the business data table.

[0011] In one embodiment, determining the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application to the power grid business resource data includes:

[0012] Based on the data call records of power grid business resource data by the power grid business front-end application, determine the number of data table calls, data table access duration, data table usage, and data table importance of any of the aforementioned business data tables;

[0013] The usage heat quantification of the business data table is obtained based on the number of times the data table is called, the access duration of the data table, the usage volume of the data table, and the importance of the data table.

[0014] In one embodiment, obtaining the usage heat quantification value of the business data table based on the number of times the data table is called, the duration of the data table access, the usage volume of the data table, and the importance of the data table includes:

[0015] The number of times the data table is called, the duration of the data table access, the amount of data table usage, and the importance of the data table are normalized to obtain a first normalized result for the number of times the data table is called, a second normalized result for the duration of the data table access, a third normalized result for the amount of data table usage, and a fourth normalized result for the importance of the data table.

[0016] The first normalization result, the second normalization result, the third normalization result, and the fourth normalization result are fused to obtain the usage heat quantification value of the business data table.

[0017] In one embodiment, fusing the first normalized result, the second normalized result, the third normalized result, and the fourth normalized result to obtain the calorific value includes:

[0018] Obtain the data table type of the business data table;

[0019] Based on a preset weight configuration mapping relationship, the weight configuration information of the data table type is determined; the weight configuration mapping relationship records the mapping relationship between multiple different data table types and multiple different weight configuration information.

[0020] According to the weights recorded in the weight configuration information, the first normalized result, the second normalized result, the third normalized result, and the fourth normalized result are weighted and summed to obtain the usage heat quantification value of the business data table.

[0021] In one embodiment, the step of processing the target popularity business data according to its distribution in the corresponding target business data table, and displaying a data popularity map of the business data table, includes:

[0022] The ratio of the data volume of the target popularity business data to the data volume of the target business data table is obtained to obtain the proportion of popularity data;

[0023] If the proportion of the popularity data is greater than a preset proportion threshold, the target business data table is split into sub-tables to obtain at least two sub-business data tables associated with the target business data table.

[0024] The data popularity information of each of the sub-business data tables is displayed in the data popularity graph of the business data table.

[0025] In one embodiment, determining the target popularity business data based on the sorting results of the business data table includes:

[0026] Based on the sorting results of the business data tables, potential hot business data are determined; the potential hot business data are the power grid business resource data in the top N business data tables ranked by the use of heat measurement values ​​in the sorting results of the business data tables; N is a positive integer greater than or equal to 1;

[0027] Remove abnormally popular business data from the potential popular business data to obtain the target popular business data; the abnormally popular business data is the power grid business resource data associated with a one-time batch query.

[0028] Secondly, this application also provides a data heat analysis device for power grid business resource data, the device comprising:

[0029] The quantification module is used to determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application to the power grid business resource data.

[0030] The sorting module is used to sort each of the business data tables according to the usage heat quantification value of each business data table, and obtain the business data table sorting result;

[0031] The filtering module is used to determine target popularity business data based on the sorting results of the business data table; the usage popularity of the target popularity business data meets preset conditions.

[0032] The processing module is used to process the target popularity business data according to the distribution of the target popularity business data in the corresponding target business data table, and display the data popularity map of the business data table.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0036] The aforementioned data heat analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power grid business resource data, by determining the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application, and sorting each business data table according to the usage heat quantification values ​​to obtain a business data table sorting result; then, based on the business data table sorting result, determining the target heat business data whose usage heat meets preset conditions; and processing the target heat business data according to the distribution of the target heat business data in its respective target business data table to display a business data table data heat map; thus, by effectively quantifying the usage heat of multiple business data tables based on the actual data call records of the power grid business front-end application, and accurately locating the target heat business data that meets preset conditions through sorting, it can effectively avoid the problem of inaccurate data heat description caused by one-sided data selection in heat analysis, and effectively improve the accuracy of describing the actual data usage status of power grid business resource data. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a diagram illustrating the application environment of a data heat analysis method for power grid business resource data in one embodiment.

[0039] Figure 2 This is a flowchart illustrating a data heat analysis method for power grid business resource data in one embodiment;

[0040] Figure 3This is a flowchart illustrating a data heat analysis method for power grid business resource data in another embodiment;

[0041] Figure 4 This is a structural block diagram of a data heat analysis device for power grid business resource data in one embodiment;

[0042] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] In practice, the collection and processing of data in this application should strictly comply with the requirements of relevant national laws and regulations, obtain the informed consent or separate consent of the data subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the data subject.

[0045] In this application, the implementation of the data scraping technology solution involved, when applied to specific products or technologies according to the above embodiments of this application, the relevant data collection, use and processing processes should comply with the requirements of national laws and regulations, conform to the principles of legality, legitimacy and necessity, not involve obtaining data types prohibited or restricted by laws and regulations, and will not hinder the normal operation of the target website.

[0046] The data heat analysis method for power grid business resource data provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, computer device 102 communicates with data center 104, which stores power grid business resource data, via a network.

[0047] In practical applications, computer device 102 can determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application for power grid business resource data; computer device 102 can sort each business data table according to the usage heat quantification values ​​of each business data table to obtain the business data table sorting results; computer device 102 can determine the target heat business data based on the business data table sorting results; the usage heat of the target heat business data meets preset conditions; computer device 102 can process the target heat business data according to the distribution of the target heat business data in its respective target business data table and display the business data table data heat map.

[0048] The computer equipment 102 may be, but is not limited to, various personal computers, laptops, tablets, and Internet of Things (IoT) devices. The data center 104 may be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0049] In one exemplary embodiment, such as Figure 2 As shown, a data heat analysis method for power grid business resource data is provided, which is then applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps S202 to S208. Wherein:

[0050] Step S202: Based on the data call records of the power grid business front-end application to the power grid business resource data, determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data.

[0051] In practice, computer equipment can determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application for power grid business resource data.

[0052] Optionally, in the process of determining the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application on the power grid business resource data, the computer equipment may determine the number of data table calls, data table access duration, data table usage, and data table importance of any of the business data tables based on the data call records of the power grid business front-end application on the power grid business resource data.

[0053] Specifically, computer equipment can count the number of times a data table is queried or accessed within a unit of time (such as hour, day, month) to obtain the number of times the data table is accessed; for data tables that require batch processing or complex calculations, the duration of a single access is recorded to obtain the data table access duration; and the actual amount of data read, downloaded, or processed during each access to the data table is counted to obtain the data table usage.

[0054] Optionally, the computer device can assign weights to different application access behaviors based on the business priorities of the front-end applications, and determine the importance of data tables based on the frequency and weight of the access behaviors. The importance of a data table can be a weighted sum of the weight assigned to an access behavior and the frequency of that access behavior.

[0055] Then, the computer device obtains a quantitative value of the usage heat of the business data table based on the number of times the data table is called, the duration of the data table access, the amount of data table usage, and the importance of the data table.

[0056] Optionally, in the process of obtaining the quantified value of the usage heat of the business data table based on the number of times the data table is called, the duration of the data table access, the amount of data table usage, and the importance of the data table, the computer device may perform normalization processing on the number of times the data table is called, the duration of the data table access, the amount of data table usage, and the importance of the data table to obtain a first normalized result for the number of times the data table is called, a second normalized result for the duration of the data table access, a third normalized result for the amount of data table usage, and a fourth normalized result for the importance of the data table.

[0057] Then, the computer device can fuse the first normalization result, the second normalization result, the third normalization result, and the fourth normalization result to obtain the usage heat quantification value of the business data table.

[0058] Optionally, during the process of fusing the first normalization result, the second normalization result, the third normalization result, and the fourth normalization result to obtain the quantified value of the heat value, the computer device may obtain the data table type of the business data table.

[0059] The computer device can determine the weight configuration information of the data table type based on the preset weight configuration mapping relationship.

[0060] The weight configuration mapping record contains the mapping relationships between multiple different data table types and multiple different weight configuration information.

[0061] The computer device can perform a weighted summation of the first normalized result, the second normalized result, the third normalized result, and the fourth normalized result according to the weights of the information records configured by the weights, to obtain the usage heat quantification value of the business data table.

[0062] Step S204: Sort each of the business data tables according to the usage heat quantification value of each business data table to obtain the business data table sorting result.

[0063] In a specific implementation, the computer device can sort each of the business data tables according to the usage heat quantification value of each business data table to obtain the business data table sorting result.

[0064] Step S206: Determine the target popularity business data based on the sorting results of the business data table; the usage popularity of the target popularity business data meets preset conditions.

[0065] Among them, target popularity business data can refer to high popularity business data.

[0066] Optionally, during the process of determining target popular business data based on the sorting results of the business data table, the computer device may also determine potential popular business data based on the sorting results of the business data table.

[0067] The potential high-intensity business data refers to the power grid business resource data in the top N business data tables ranked by the highest heat-quantitative value in the business data table sorting results; N is a positive integer greater than or equal to 1. In practical applications, potential high-intensity business data can refer to potentially high-intensity data. In practical applications, potentially high-intensity data can be the power grid business resource data in the top 10% of the business data table sorting results.

[0068] Then, the computer device can remove abnormally popular business data from the potential popular business data to obtain the target popular business data.

[0069] The abnormal heat data refers to the power grid business resource data associated with a one-time batch query.

[0070] Then, the computer equipment can obtain the rules needed for data popularity calculation and determine the business data tables that have been consistently ranked at the top of the sorting list in the processed business data tables (such as real-time electricity price tables and transmission line status tables); then, from the business data tables that have been consistently ranked at the top of the sorting list in the long term, determine the business data tables related to the core business (such as fault repair records and user electricity consumption details).

[0071] Step S208: Based on the distribution of the target popularity business data in the corresponding target business data table, process the target popularity business data and display the data popularity map of the business data table.

[0072] In practice, computer equipment can process the target popularity data according to the distribution of the target popularity data in the corresponding target business data table, and display the data popularity map of the business data table.

[0073] Optionally, during the process of processing the target popularity business data according to its distribution in the corresponding target business data table and displaying the data heatmap of the business data table, the computer device can obtain the ratio between the data volume of the target popularity business data and the data volume of the target business data table to obtain the popularity data proportion; if the popularity data proportion is greater than a preset proportion threshold, the target business data table is split into sub-tables to obtain at least two sub-business data tables associated with the target business data table.

[0074] Specifically, the computer system distributes data across multiple sub-tables based on the hash value of the user ID (e.g., if the hash value of the user ID is modulo 16, it is divided into 16 sub-tables). This ensures that the target sub-table can be quickly located during join queries, reducing the load on a single table. The calculation rule is: sub-table number = hash(user ID) % 16. For example, if the hash value of user ID = 10086 is modulo 3, then the data is stored in the user_data_3 table.

[0075] Computer equipment can display the data popularity information of each sub-business data table in the data popularity graph of the business data table. In practical applications, computer equipment can use color intensity to represent the popularity value of different data tables (e.g., red represents high popularity, blue represents low popularity), and overlay it on the power grid business architecture diagram to effectively show the relationship between data popularity and business modules.

[0076] In the aforementioned data heat analysis method for power grid business resource data, the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data are determined based on the data call records of the power grid business front-end application. The business data tables are then sorted according to these usage heat quantification values ​​to obtain a sorting result. Based on this sorting result, target heat business data that meets preset usage conditions is identified. Furthermore, the target heat business data is processed based on its distribution within its respective target business data table, and a data heat map of the business data table is displayed. Thus, by effectively quantifying the usage heat of multiple business data tables based on the actual data call records of the power grid business front-end application, and accurately locating target heat business data that meets preset conditions through sorting, the method effectively avoids the problem of inaccurate data heat description caused by biased data selection in heat analysis, and effectively improves the accuracy of describing the actual data usage status of power grid business resource data.

[0077] In another embodiment, such as Figure 3 As shown, a data heat analysis method for power grid business resource data is provided, which is then applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0078] Step S302: Based on the data call records of the power grid business front-end application for power grid business resource data, determine the number of data table calls, data table access duration, data table usage, and data table importance of any business data table.

[0079] Step S304: Based on the number of times the data table is called, the duration of the data table access, the amount of data table used, and the importance of the data table, obtain the quantitative value of the usage heat of the business data table.

[0080] Step S306: Sort each business data table according to the usage heat quantification value of each business data table to obtain the business data table sorting result.

[0081] Step S308: Determine potential hot business data based on the sorting results of the business data tables; potential hot business data are the power grid business resource data in the top N business data tables ranked by the heat quantification value in the sorting results of the business data tables; N is a positive integer greater than or equal to 1.

[0082] Step S310: Remove abnormal hot business data from the potential hot business data to obtain the target hot business data; the abnormal hot business data is the power grid business resource data associated with a one-time batch query.

[0083] Step S312: Based on the distribution of target popularity business data in the corresponding target business data table, process the target popularity business data and display the data popularity map of the business data table.

[0084] It should be noted that the specific limitations of the above steps can be found in the specific limitations of the data heat analysis method for power grid business resource data described above.

[0085] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0086] Based on the same inventive concept, this application also provides a data heat analysis device for power grid business resource data to implement the data heat analysis method for power grid business resource data involved above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the data heat analysis device for power grid business resource data provided below can be found in the limitations of the data heat analysis method for power grid business resource data above, and will not be repeated here.

[0087] In one exemplary embodiment, such as Figure 4 As shown, a data heat analysis device for power grid business resource data is provided, comprising:

[0088] The quantification module 410 is used to determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application to the power grid business resource data.

[0089] The sorting module 420 is used to sort each of the business data tables according to the usage heat quantification value of each of the business data tables, and obtain the business data table sorting result;

[0090] The filtering module 430 is used to determine target popularity business data based on the sorting results of the business data table; the usage popularity of the target popularity business data meets preset conditions.

[0091] The processing module 440 is used to process the target popularity business data according to the distribution of the target popularity business data in the corresponding target business data table, and display the data popularity map of the business data table.

[0092] In one embodiment, the quantization module 410 is specifically used to determine the number of times a business data table is called, the duration of data table access, the amount of data table used, and the importance of data table based on the data call records of the power grid business front-end application for power grid business resource data; and to obtain a quantified value of the usage heat of the business data table based on the number of times a data table is called, the duration of data table access, the amount of data table used, and the importance of data table.

[0093] In one embodiment, the quantization module 410 is specifically used to normalize the number of times the data table is called, the duration of the data table access, the amount of data table usage, and the importance of the data table to obtain a first normalized result of the number of times the data table is called, a second normalized result of the duration of the data table access, a third normalized result of the amount of data table usage, and a fourth normalized result of the importance of the data table; and to fuse the first normalized result, the second normalized result, the third normalized result, and the fourth normalized result to obtain a quantified value of the usage heat of the business data table.

[0094] In one embodiment, the quantization module 410 is specifically used to obtain the data table type of the business data table; determine the weight configuration information of the data table type based on a preset weight configuration mapping relationship; the weight configuration mapping relationship records the mapping relationship between multiple different data table types and multiple different weight configuration information; and perform a weighted summation of the first normalization result, the second normalization result, the third normalization result, and the fourth normalization result according to the weight recorded in the weight configuration information to obtain the usage heat quantification value of the business data table.

[0095] In one embodiment, the processing module 440 is specifically used to obtain the ratio between the data volume of the target popularity business data and the data volume of the target business data table to obtain the popularity data ratio; if the popularity data ratio is greater than a preset ratio threshold, the target business data table is split into sub-business data tables to obtain at least two sub-business data tables associated with the target business data table; and the popularity information of each sub-business data table is displayed in the business data table popularity graph.

[0096] In one embodiment, the filtering module 430 is specifically used to determine potential hot business data based on the sorting result of the business data table; the potential hot business data are power grid business resource data in the top N business data tables ranked by the heat quantification value in the sorting result of the business data table; N is a positive integer greater than or equal to 1; remove abnormal hot business data from the potential hot business data to obtain the target hot business data; the abnormal hot business data are power grid business resource data associated with a one-time batch query.

[0097] The modules in the aforementioned data heat analysis device for power grid business resource data can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0098] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a data heat analysis method for power grid business resource data. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0099] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the data heat analysis method for power grid business resource data described above. The steps of the data heat analysis method for power grid business resource data here can be the steps in the data heat analysis method for power grid business resource data from the various embodiments described above.

[0101] In one embodiment, a computer-readable storage medium is provided, storing a computer program that, when executed by a processor, causes the processor to perform the steps of the data heat analysis method for power grid business resource data described above. The steps of the data heat analysis method for power grid business resource data here can be the steps in the data heat analysis method for power grid business resource data from the various embodiments described above.

[0102] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, causes the processor to perform the steps of the data heat analysis method for power grid business resource data described above. The steps of the data heat analysis method for power grid business resource data here can be the steps in the data heat analysis method for power grid business resource data from the various embodiments described above.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0104] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic resistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0106] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing the heat of data in power grid business resource data, characterized in that, The method includes: Based on the data call records of the power grid business front-end application to the power grid business resource data, determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data; Based on the usage heat quantification value of each business data table, sort each business data table to obtain the business data table sorting result; Based on the sorting results of the business data table, target popularity business data is determined; the usage popularity of the target popularity business data meets preset conditions. Based on the distribution of the target popularity business data in the corresponding target business data table, the target popularity business data is processed to display a data heat map of the business data table.

2. The method according to claim 1, characterized in that, The step of determining the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application for power grid business resource data includes: Based on the data call records of power grid business resource data by the power grid business front-end application, determine the number of data table calls, data table access duration, data table usage, and data table importance of any of the aforementioned business data tables; The usage heat quantification of the business data table is obtained based on the number of times the data table is called, the access duration of the data table, the usage volume of the data table, and the importance of the data table.

3. The method according to claim 2, characterized in that, The step of obtaining the usage heat quantification value of the business data table based on the number of times the data table is called, the duration of the data table access, the usage volume of the data table, and the importance of the data table includes: The number of times the data table is called, the duration of the data table access, the amount of data table usage, and the importance of the data table are normalized to obtain a first normalized result for the number of times the data table is called, a second normalized result for the duration of the data table access, a third normalized result for the amount of data table usage, and a fourth normalized result for the importance of the data table. The first normalization result, the second normalization result, the third normalization result, and the fourth normalization result are fused to obtain the usage heat quantification value of the business data table.

4. The method according to claim 3, characterized in that, The process of fusing the first normalization result, the second normalization result, the third normalization result, and the fourth normalization result to obtain the calorific value includes: Obtain the data table type of the business data table; Based on a preset weight configuration mapping relationship, the weight configuration information of the data table type is determined; the weight configuration mapping relationship records the mapping relationship between multiple different data table types and multiple different weight configuration information. According to the weights recorded in the weight configuration information, the first normalized result, the second normalized result, the third normalized result, and the fourth normalized result are weighted and summed to obtain the usage heat quantification value of the business data table.

5. The method according to claim 1, characterized in that, The step of processing the target popularity data based on its distribution within its respective target business data table, and displaying a data heatmap of the business data table, includes: The ratio of the data volume of the target popularity business data to the data volume of the target business data table is obtained to obtain the proportion of popularity data; If the proportion of the popularity data is greater than a preset proportion threshold, the target business data table is split into sub-tables to obtain at least two sub-business data tables associated with the target business data table. The data popularity information of each of the sub-business data tables is displayed in the data popularity graph of the business data table.

6. The method according to any one of claims 1 to 5, characterized in that, The step of determining the target popularity business data based on the sorting results of the business data table includes: Based on the sorting results of the business data tables, potential hot business data are determined; the potential hot business data are the power grid business resource data in the top N business data tables ranked by the use of heat measurement values ​​in the sorting results of the business data tables; N is a positive integer greater than or equal to 1; Remove abnormally popular business data from the potential popular business data to obtain the target popular business data; the abnormally popular business data is the power grid business resource data associated with a one-time batch query.

7. A data heat analysis device for power grid business resource data, characterized in that, The device includes: The quantification module is used to determine the usage heat quantification values ​​of at least two business data tables associated with the power grid business resource data based on the data call records of the power grid business front-end application to the power grid business resource data. The sorting module is used to sort each of the business data tables according to the usage heat quantification value of each business data table, and obtain the business data table sorting result; The filtering module is used to determine target popularity business data based on the sorting results of the business data table; the usage popularity of the target popularity business data meets preset conditions. The processing module is used to process the target popularity business data according to the distribution of the target popularity business data in the corresponding target business data table, and display the data popularity map of the business data table.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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